A Precautionary Approach to Genetically Modified Organisms: Challenges and Implications for Policy and Science

The commercial introduction of genetically modified organisms (GMOs) has revealed a broad range of views among scientists and other stakeholders on perspectives of genetic engineering (GE) and if and how GMOs should be regulated. Within this controversy, the precautionary principle has become a contentious issue with high support from skeptical groups but resisted by GMO advocates. How to handle lack of scientific understanding and scientific disagreement are core issues within these debates. This article examines some of the key issues affecting precaution as a legal standard and as an approach to the use of science in decision-making processes. It is pointed out that there is a need for reflection over the level of scientific evidence required for applying the precautionary principle as well as who should have the burden of proof when there are uncertainties. Further, an awareness of the broader scientific uncertainties found in GMO risk assessment implies that a precautionary approach must be elaborated: both for acknowledging uncertainties and for identification of scientific responses. Since precaution is an important issue within the sustainable development framework, it is suggested that sustainability can provide a normative standard that can help to reveal the influence and negotiate the importance of the various forms of uncertainty. Wise management of uncertainties and inclusion of normative aspects in risk assessment and management may help to ensure sustainable and socially robust GMO innovations at present and in the future.     

A participatory decision making framework for artisanal fisheries collaborative governance: Insights from management committees in Chile

Fisheries management is increasingly transitioning towards collaborative governance. Collaborative systems depend on stakeholders’ capacity to design and implement legitimate and scientifically robust management plans within collective action arenas. Here we propose that collaborative governance outcomes, in fisheries management, will benefit from using structured participatory decision making frameworks that enhance deliberative thinking among stakeholders. We tested our approach in the artisanal fishery of Chile, an important producer of marine resources. Recently in 2013, Chile made important changes to fisheries policies by creating multi-sectorial management committees to manage de facto open access fishing areas. We applied a structured decision making framework to inform the restructuring of a management plan within a committee. As a result, we identified goals,objectives and indicators, including social, economic, biological and ecological dimensions; we explored tradeoffs, assessing the relative importance of the objectives; finally, we created scenarios and prioritized alternatives, reflecting on the interplay between self-regulation and government control. Members of the management committee were able to rationalize the different steps of the framework and identify ways forward which highlighted the importance of self-regulation in comparison to central authorities’ control. We concluded that structured decision making promotes spaces for rational analysis of alternatives costs and benefits. Promoting deliberative thinking in fisheries management can improve equity, legitimacy and sustainability of collaborative governance.     

Sustainability of fisheries through marine reserves: a robust modeling analysis

Among the many factors that contribute to overexploitation of marine fisheries, the role played by uncertainty is important. This uncertainty includes both the scientific uncertainties related to the resource dynamics or assessments and the uncontrollability of catches. Some recent works advocate for the use of marine reserves as a central element of future stock management. In the present paper, we study the influence of protected areas upon fisheries sustainability through a simple dynamic model integrating non-stochastic harvesting uncertainty and a constraint of safe minimum biomass level. Using the mathematical concept of invariance kernel in a robust and worst-case context, we examine through a formal modeling analysis how marine reserves might guarantee viable fisheries. We also show how sustainability requirement is not necessarily conflicting with optimization of catches. Numerical simulations are provided to illustrate the main findings.     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.     

Adaptive management in the U.S. National Wildlife Refuge System: science-management partnerships for conservation delivery

Adaptive management is an approach to recurrent decision making in which uncertainty about the decision is reduced over time through comparison of outcomes predicted by competing models against observed values of those outcomes. The National Wildlife Refuge System (NWRS) of the U.S. Fish and Wildlife Service is a large land management program charged with making natural resource management decisions, which often are made under considerable uncertainty, severe operational constraints, and conditions that limit ability to precisely carry out actions as intended. The NWRS presents outstanding opportunities for the application of adaptive management, but also difficult challenges. We describe two cooperative programs between the Fish and Wildlife Service and the U.S. Geological Survey to implement adaptive management at scales ranging from small, single refuge applications to large, multi-refuge, multi-region projects. Our experience to date suggests three important attributes common to successful implementation: a vigorous multi-partner collaboration, practical and informative decision framework components, and a sustained commitment to the process. Administrators in both agencies should consider these attributes when developing programs to promote the use and acceptance of adaptive management in the NWRS.     

Joint operation rules considering the uncertainty of energy available for multiple cascaded hydropower system

Multi-reservoir operation rules have been widely used in practice operation. The operation rules are often derived from historical or simulated run-off information through implicit stochastic optimization or parameterization-simulation-optimization. The output decisions of operation rules are usually obtained without considering inflow forecasting or using perfect runoff forecast information, which is hardly implemented in practical applications. This paper proposes robust joint operation rules for multiple cascaded reservoirs considering the uncertainty of energy available. The rule parameters are optimized using multi-step genetic algorithm for minimum-power maximization. A case study for a three-cascaded-reservoirs system shows that, compared with deterministic joint operation rules, the accuracy of energy available estimation of joint operation rules is increased by 2.3%, considering inflow uncertainty. The simulated minimum-power decision of joint operation rules considering the uncertainty of energy available is 40.4% higher than that of determinate joint operation rules. Results indicate that there is a possibility of obtaining greater and more effective power decisions through the joint operation rules considering the uncertainty of available energy.     

Irreversible Investment in Wetlands Preservation: Optimal Ecosystem Restoration Under Uncertainty

The model, a stochastic dynamic program, is used to optimize the timing and type of protective structure under a range of management goals. A wetland can either be optimal for fish or optimal for mammals and waterfowl, but not both. Because credible estimates of the economic values of wetland services do not exist, we treat those values as parameters in a multiobjective analysis and show the decisions implied by alternative valuations. The model is applied to the case of Metzger Marsh, a Lake Erie coastal wetland near Toledo, Ohio, where the decision was made in 1993 to construct an open dike. We find that the optimal decision is robust with respect to varying assumptions about the formation of barrier beaches and the probability of climate change, but that the decision is not robust to assumptions concerning the health of an unprotected Metzger Marsh. The most important source of uncertainty is the biological health of an unprotected wetland.     

Uncertainty and risk in wildland fire management: a review

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to treatments, and of spatiotemporal dynamics involving disturbance regimes and climate change. This work attempts to systematically align sources of uncertainty with the most appropriate decision support methodologies, in order to facilitate cost-effective, risk-based wildfire planning efforts. We review the state of wildfire risk assessment and management, with a specific focus on uncertainties challenging implementation of integrated risk assessments that consider a suite of human and ecological values. Recent advances in wildfire simulation and geospatial mapping of highly valued resources have enabled robust risk-based analyses to inform planning across a variety of scales, although improvements are needed in fire behavior and ignition occurrence models. A key remaining challenge is a better characterization of non-market resources at risk, both in terms of their response to fire and how society values those resources. Our findings echo earlier literature identifying wildfire effects analysis and value uncertainty as the primary challenges to integrated wildfire risk assessment and wildfire management. We stress the importance of identifying and characterizing uncertainties in order to better quantify and manage them. Leveraging the most appropriate decision support tools can facilitate wildfire risk assessment and ideally improve decision-making.     

Approaches to Evaluate Water Quality Model Parameter Uncertainty for Adaptive TMDL Implementation1

Abstract: The National Research Council recommended Adaptive Total Maximum Daily Load implementation with the recognition that the predictive uncertainty of water quality models can be high. Quantifying predictive uncertainty provides important information for model selection and decision‐making. We review five methods that have been used with water quality models to evaluate model parameter and predictive uncertainty. These methods (1) Regionalized Sensitivity Analysis, (2) Generalized Likelihood Uncertainty Estimation, (3) Bayesian Monte Carlo, (4) Importance Sampling, and (5) Markov Chain Monte Carlo (MCMC) are based on similar concepts; their development over time was facilitated by the increasing availability of fast, cheap computers. Using a Streeter‐Phelps model as an example we show that, applied consistently, these methods give compatible results. Thus, all of these methods can, in principle, provide useful sets of parameter values that can be used to evaluate model predictive uncertainty, though, in practice, some are quickly limited by the “curse of dimensionality” or may have difficulty evaluating irregularly shaped parameter spaces. Adaptive implementation invites model updating, as new data become available reflecting water‐body responses to pollutant load reductions, and a Bayesian approach using MCMC is particularly handy for that task.     

Pragmatic Approaches for Water Management Under Climate Change Uncertainty1

Stakhiv, Eugene Z., 2011. Pragmatic Approaches for Water Management Under Climate Change Uncertainty. Journal of the American Water Resources Association (JAWRA) 47(6):1183–1196. DOI: 10.1111/j.1752‐1688.2011.00589.x Abstract: Water resources management is in a difficult transition phase, trying to accommodate large uncertainties associated with climate change while struggling to implement a difficult set of principles and institutional changes associated with integrated water resources management. Water management is the principal medium through which projected impacts of global warming will be felt and ameliorated. Many standard hydrological practices, based on assumptions of a stationary climate, can be extended to accommodate numerous aspects of climate uncertainty. Classical engineering risk and reliability strategies developed by the water management profession to cope with contemporary climate uncertainties can also be effectively employed during this transition period, while a new family of hydrological tools and better climate change models are developed. An expansion of the concept of “robust decision making,” coupled with existing analytical tools and techniques, is the basis for a new approach advocated for planning and designing water resources infrastructure under climate uncertainty. Ultimately, it is not the tools and methods that need to be revamped as much as the suite of decision rules and evaluation principles used for project justification. They need to be aligned to be more compatible with the implications of a highly uncertain future climate trajectory, so that the hydrologic effects of that uncertainty are correctly reflected in the design of water infrastructure.     

Improving Conservation of Florida Manatees (Trichechus manatus latirostris): Conceptualization and Contributions Toward a Regional Warm-Water Network Management Strategy for Sustainable Winter Habitat

We used southwestern Florida as a case study to lay the groundwork for an intended and organized decision-making process for managing warm-water habitat needed by endangered manatees to survive winters in Florida. Scientists and managers have prioritized (a) projecting how the network of warm-water sites will change over the next 50 years as warmed industrial discharges may expire and as flows of natural springs are reduced through redirection of water for human uses, and (b) mitigating such changes to prevent undue consequences to manatees. Given the complexities introduced by manatee ecology; agency organizational structure; shifting public demands; fluctuating resource availability; and managing within interacting cultural, social, political, and environmental contexts, it was clear that a structured decision process was needed. To help promote such a process, we collected information relevant to future decisions including maps of known and suspected warm-water sites and prototyped a characterization of sites and networks. We propose steps that would lead to models that might serve as core tools in manatee/warm-water decision-making, and we summarized topics relevant for informed decision-making (e.g., manatee spatial cognition, risk of cold-stress morbidity and mortality, and human dimensions). A major impetus behind this effort is to ensure proactively that robust modeling tools are available well in advance of the anticipated need for a critical management decision.     

Land Resources Allocation Strategies in an Urban Area Involving Uncertainty: A Case Study of Suzhou,in the Yangtze River Delta of China

A large number of mathematical models have been developed to support land resource allocation decisions and land management needs; however, few of them can address various uncertainties that exist in relation to many factors presented in such decisions (e.g., land resource availabilities, land demands, land-use patterns, and social demands, as well as ecological requirements). In this study, a multi-objective interval-stochastic land resource allocation model (MOISLAM) was developed for tackling uncertainty that presents as discrete intervals and/or probability distributions. The developed model improves upon the existing multi-objective programming and inexact optimization approaches. The MOISLAM not only considers economic factors, but also involves food security and eco-environmental constraints; it can, therefore, effectively reflect various interrelations among different aspects in a land resource management system. Moreover, the model can also help examine the reliability of satisfying (or the risk of violating) system constraints under uncertainty. In this study, the MOISLAM was applied to a real case of long-term urban land resource allocation planning in Suzhou, in the Yangtze River Delta of China. Interval solutions associated with different risk levels of constraint violation were obtained. The results are considered useful for generating a range of decision alternatives under various system conditions, and thus helping decision makers to identify a desirable land resource allocation strategy under uncertainty.     

Assessing the Effects of Management Alternatives on Habitat Suitability in a Forested Landscape of Northeastern China

Forest management often has cumulative, long-lasting effects on wildlife habitat suitability and the effects may be impractical to evaluate using landscape-scale field experiments. To understand such effects, we linked a spatially explicit landscape disturbance and succession model (LANDIS) with habitat suitability index (HSI) models to assess the effects of management alternatives on habitat suitability in a forested landscape of northeastern China. LANDIS was applied to simulate future forest landscape changes under four management alternatives (no cutting, clearcutting, selective cutting I and II) over a 200-year horizon. The simulation outputs were linked with HSI models for three wildlife species, the red squirrel (Sciurus vulgaris), the red deer (Cervus elaphus) and the hazel grouse (Bonasa bonasia). These species are chosen because they represent numerous species that have distinct habitat requirements in our study area. We assessed their habitat suitability based on the mean HSI values, which is a measure of the average habitat quality. Our simulation results showed that no one management scenario was the best for all species and various forest management scenarios would lead to conflicting wildlife habitat outcomes. How to choose a scenario is dependent on the trade-off of economical, ecological and social goals. Our modeling effort could provide decision makers with relative comparisons among management scenarios from the perspective of biodiversity conservation. The general simulation results were expected based on our knowledge of forest management and habitat relationships of the species, which confirmed that the coupled modeling approach correctly simulated the assumed relationships between the wildlife, forest composition, age structure, and spatial configuration of habitat. However, several emergent results revealed the unexpected outcomes that a management scenario may lead to.     

Sustainable pattern analysis of a publicly owned Material Recovery Facility in a fast-growing urban setting under uncertainty

Sustainable development goals are achievable through the installation of Material Recovery Facilities (MRFs) in certain solid waste management systems, especially those in rapidly expanding multi-district urban areas. MRFs are a cost-effective alternative when curbside recycling does not demonstrate long-term success. Previous capacity planning uses mixed integer programming optimization for the urban center of the city of San Antonio, Texas to establish that a publicly owned material recovery facility is preferable to a privatized facility. As a companion study, this analysis demonstrates that a MRF alleviates economic, political, and social pressures facing solid waste management under uncertainty. It explores the impact of uncertainty in decision alternatives in an urban environmental system. From this unique angle, waste generation, incidence of recyclables in the waste stream, routing distances, recycling participation, and other planning components are taken as intervals to expand upon previous deterministic integer-programming models. The information incorporated into the optimization objectives includes economic impacts for recycling income and cost components in waste management. The constraint set consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening restrictions. Due to the fragmented data set, a grey integer programming modeling approach quantifies the consequences of inexact information as it propagates through the final solutions in the optimization process. The grey algorithm screens optimal shipping patterns and an ideal MRF location and capacity. Two case settings compare MRF selection policies where optimal solutions exemplify the value of grey programming in the context of integrated solid waste management.     

Evaluation Criteria for Participatory Research: Insights from Coastal Uruguay

Participatory research in which experts and non-experts are co-researchers in addressing local concerns (also known as participatory action research or community-based research) can be a valuable approach for dealing with the uncertainty of social–ecological systems because it fosters learning among stakeholders and co-production of knowledge. Despite its increased application in the context of natural resources and environmental management, evaluation of participatory research has received little attention. The objectives of this research were to define criteria to evaluate participatory research processes and outcomes, from the literature on participation evaluation, and to apply them in a case study in an artisanal fishery in coastal Uruguay. Process evaluation criteria (e.g., problem to be addressed of key interest to local and additional stakeholders; involvement of interested stakeholder groups in every research stage; collective decision making through deliberation; and adaptability through iterative cycles) should be considered as conditions to promote empowering participatory research. Our research contributes to knowledge on evaluation of participatory research, while also providing evidence of the positive outcomes of this approach, such as co-production of knowledge, learning, strengthened social networks, and conflict resolution.     

A national critical loads framework for atmospheric deposition effects assessment: IV. Model selection,applications, and critical loads mapping

The critical loads approach is emerging as an attractive means for evaluating the effects of atmospheric deposition on sensitive terrestrial and aquatic ecosystems. Various approaches are available for modeling ecosystem responses to deposition and for estimating critical load values. These approaches include empirical and statistical relationships, steady-state and simple process models, and integrated-effects models. For any given ecosystem, the most technically sophisticated approach will not necessarily be the most appropriate for all applications; identification of the most useful approach depends upon the degree of accuracy needed and upon data and computational requirements, biogeochemical processes being modeled, approaches used for representing model results on regional bases, and desired degree of spatial and temporal resolution. Different approaches are characterized by different levels of uncertainty. If the limitations of individual approaches are known, the user can determine whether an approach provides a reasonable basis for decision making. Several options, including point maps, grid maps, and ecoregional maps, are available for presenting model results in a regional context. These are discussed using hypothetical examples for choosing populations and damage limits. The research described in this article has been funded by the US Environmental Protection Agency. This document has been prepared at the EPA Environmental Research Laboratory in Corvallis, Oregon, through contract #68-C8-0006 with ManTech Environmental Technology, Inc., and Interagency Agreement #1824-B014-A7 with the U.S. Department of Energy and at Oak Ridge National Laboratory managed by Martin Marietta Energy Systems, Inc., under Contract DE-AC05-84OR21400 with the US Department of Energy. Environmental Sciences Division Publication No. 3904. It has been subjected to the agency’s peer and administrative review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.     

Enhancing perspectives on lake impairments using satellite observations: A case study on High Rock Lake,North Carolina

Stakeholders developing water quality improvement plans for lakes and reservoirs are challenged by the sparsity of in-situ data and the uncertainty ingrained in management decisions. This study explores how satellite images can fill gaps in water quality databases and provide more holistic assessments of impairments. The study site is an impaired water body that is serving as a pilot for improving state-wide nutrient management planning processes. An existing in-situ database was used to calibrate semi-analytical models that relate satellite reflectance values to turbidity and total suspended solids (TSS). Landsat-7 images from 1999 to 2020 that overpass High Rock Lake, North Carolina were downloaded and processed, providing 42 turbidity and 39 TSS satellite and in-situ match-ups for model calibration and validation. Model r-squared values for the fitted turbidity and TSS models are 0.72 and 0.74, and the mean absolute errors are 14.6 NTU and 3.2 mg/L. The satellite estimates were compared to the in-situ data and simulated TSS values produced by a calibrated hydrologic-hydrodynamic model. The process-based model is considered less accurate than the satellite model based on statistical performance metrics. Comparisons between data sources are illustrated with time series plots, frequency curves, and aggregate decision metrics to highlight the dependence of lake impairment assessments on the spatial and temporal frequency of available data and model accuracy.     

Bringing diverse knowledge sources together--a meta-model for supporting integrated catchment management

Integrated catchment management (ICM), as promoted by recent legislation such as the European Water Framework Directive, presents difficult challenges to planners and decision-makers. To support decision-making in the face of high complexity and uncertainty, tools are required that can integrate the evidence base required to evaluate alternative management scenarios and promote communication and social learning. In this paper we present a pragmatic approach for developing an integrated decision-support tool, where the available sources of information are very diverse and a tight model coupling is not possible. In the first instance, a loosely coupled model is developed which includes numerical sub-models and knowledge-based sub-models. However, such a model is not easy for decision-makers and stakeholders to operate without modelling skills. Therefore, we derive from it a meta-model based on a Bayesian Network approach which is a decision-support tool tailored to the needs of the decision-makers and is fast and easy to operate. The meta-model can be derived at different levels of detail and complexity according to the requirements of the decision-makers. In our case, the meta-model was designed for high-level decision-makers to explore conflicts and synergies between management actions at the catchment scale. As prediction uncertainties are propagated and explicitly represented in the model outcomes, important knowledge gaps can be identified and an evidence base for robust decision-making is provided. The framework seeks to promote the development of modelling tools that can support ICM both by providing an integrated scientific evidence base and by facilitating communication and learning processes.     

Multi-criteria risk management with the use of <Emphasis Type="Italic">DecernsMCDA</Emphasis>: methods and case studies

Emerging challenges of risk management, environmental protection, and land-use planning requires integration of stakeholder values and expert judgment. The process of decision making in situation of high uncertainty can be assisted through the use of decision support systems (DSSs). Such DSSs are often based on tools for spatial data representation (GIS) and environmental models that are integrated using multi-criteria decision analysis (MCDA). This paper presents DecernsMCDA implementing all major types of multi-criteria methods and tools (AHP, MAUT, Outranking) under the same user interface. In addition to providing ability for testing model uncertainty associated with selection of specific MCDA algorithms, DecernsMCDA implements new algorithms for parameter uncertainty analysis based on probabilistic approaches and fuzzy sets. The paper illustrates application of DecernsMCDA for selecting remedial alternative at radiologically contaminated sites.     

More is not always better: coping with ambiguity in natural resources management

Coping with ambiguities in natural resources management has become unavoidable. Ambiguity is a distinct type of uncertainty that results from the simultaneous presence of multiple valid, and sometimes conflicting, ways of framing a problem. As such, it reflects discrepancies in meanings and interpretations. Under the presence of ambiguity it is not clear what problem is to be solved, who should be involved in the decision processes or what is an appropriate course of action. Despite the extensive literature about methodologies and tools to deal with uncertainty, not much has been said about how to handle ambiguities. In this paper, we discuss the notions of framing and ambiguity, and we identify five broad strategies to handle it: rational problem solving, persuasion, dialogical learning, negotiation and opposition. We compare these approaches in terms of their assumptions, mechanisms and outcomes and illustrate each approach with a number of concrete methods.